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BFSI
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Generative AI
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Healthcare
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Text AI
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Retail and E-Commerce
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Voice AI
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Manufacturing
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Entertainment & Broadcasting
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January 24, 2024
Shilpa Ramaswamy
How is AI Changing the Role of Business Analysts as we Know Them?
Due to the advancements in AI-powered tools, business analysts now have the opportunity to automate tasks that were traditionally performed manually, like data collection and analysis. So how will the role of a business analyst change as we know it, find out here.
Due to the advancements in AI-powered tools, business analysts now have the opportunity to automate tasks that were traditionally performed manually, like data collection and analysis. As a result, they can dedicate more time and energy to strategic work, such as identifying potential business opportunities and fostering innovation within their organisations. This shift allows professionals in this field to make more impactful contributions to their companies’ overall success and growth.
Therefore, business analysts will continue to be in demand for the foreseeable future. However, their responsibilities and expectations are evolving, necessitating a more comprehensive understanding of the business landscape and adept communication skills with stakeholders. Moreover, they must develop critical thinking abilities alongside creative problem-solving skills.
So how will AI, which has eliminated specific tasks of business analysts, support them as they take on newer challenges? Let’s find out.
An Example of how a Business Analyst’s Role has Changed with AI
For instance, analysts tasked with optimising ship routes may now build fuel models and interpret their outputs, calculate carbon emissions, predict weather patterns in a changing climate, and collaborate with captains who are subject matter experts. The analyst must also be a storyteller and business strategist focused on delivering value to get revenue growth for their findings.
The role of analysts is undergoing a revolution thanks to AI-driven data exploration. Instead of just creating pivot tables and 2D graphs, analysts now deliver actionable insights that instil greater confidence in senior management and data science teams. They can provide more informed and strategic direction with AI data analysis.
In this way, businesses can extract more value from their data infrastructure investments, even if they need help finding qualified data scientists for their projects.
Here are 3 Ways that Business Analysts can use AI in their Day-to-Day Work
1. Glean Deeper Insights from Data Sources
With the help of AI, analysts no longer have to sift through large datasets with numerous columns manually. Instead, they can rely on no-code AI solutions to extract meaningful insights from various data sources, whether proprietary, third-party, or public. These discoveries are presented in intuitive visualisations and easy-to-understand language.
By leveraging AI technology, analysts can now focus their expertise on interpreting these findings within the context of their specific industry and business needs. For example, in supply chain management, AI can uncover connections between historical sales data, market trends like interest rates, and customer feedback signals. Analysts can then consider additional factors such as product life cycle stages, pending legislation and regulations, seasonal demand variations, upcoming sales promotions, updates on competitors’ product launches, and emerging technologies.
Overall, AI empowers analysts by automating the initial discovery process and allowing them to apply their institutional knowledge to filter through and interpret the generated insights. This combination of advanced technology and human expertise leads to more accurate analysis and informed decision-making.
2. Looking at Data Insights from Various Sources
The underutilisation of data is a common issue among business leaders. While traditional methods relied on basic metrics and spreadsheets, AI has revolutionised data analysis. By exploring data holistically with machine learning, analysts can uncover new insights that were previously overlooked.
For example, instead of solely relying on customer demographics to drive sales volume, analysts can now consider other factors, such as employee data. This allows them to determine if an increase in employees significantly impacts sales more than customer personas. Additionally, they can examine the effect of employee experience on sales performance.
Another area of exploration is product differentiation. Is a wide range of choices associated with higher sales? Or are sales cycles shorter and profits higher if you focus on a limited selection?
AI empowers analysts by providing a comprehensive view of all data points and revealing where different factors intersect. These fully informed insights enable businesses to make more strategic decisions based on concrete evidence. Their findings might reveal for instance, that single, urban graduates are more likely to purchase in-store on Thursdays when the sales staff have an average experience of 3.8 years.
3. Deciding the Feasibility of Business Projects in the Pilot Phase
Analysts can now identify potential use cases and select the most valuable ones from the available data using artificial intelligence. This allows them to make informed decisions before allocating precious data science resources or pursuing costly endeavours that may yield negative results.
This development is truly transformative. Rather than present hypothetical scenarios with numerous caveats, analysts can provide insights based on interconnected data.
In a high-volume industry, an AI data exploration analyst discovers that customers who don’t engage with training materials are more likely to discontinue their services. With this information, the analyst can decide whether retaining such customers is worth the investment.
In this way, our analyst can determine whether implementing a proactive model to identify high-churn customers and launching an automated training program would be a wise or unprofitable investment before the pilot phase.
How to Support Your Business Analysts to Adopt AI Faster
Explore training programs designed to equip your teams with a deep understanding of AI concepts. Train your analysts on cutting-edge tools that extend beyond the confines of traditional BI dashboards. Ensure they are well-versed on functionalities such as exploring numerous data attributes simultaneously, immersive visualisations that bring data to life, automated reporting and data storytelling for seamless insights sharing.
Shine a spotlight on the achievements of your early adopter analysts to cultivate a rewards and recognition culture. You will motivate the entire data team to envision what’s achievable by showcasing these AI data superheroes.
Revise your organizational policies and procedures to embrace the potential of AI. Consider those policies that welcome experimentation and foster innovation.
Ensure your analysts take on cross-functional and strategic roles by appointing a dedicated individual to oversee this transition. Make it easier for them to communicate and collaborate across all departments and levels.
Constantly communicate the enduring principles that remain unchanged. While AI augments business decisions, the core foundation relies on human judgment. Corporate mission, societal impact, and business priorities will forever influence these decisions.
Conclusion
The rapid advancement of AI has sparked discussions regarding its potential to replace business analysts. Proponents argue that AI can enhance efficiency by automating data analysis and insights generation. However, opponents emphasize that the business analyst role extends beyond data analysis, encompassing critical thinking, problem-solving, and human interaction skills.
Business analysts excel in contextual understanding and stakeholder engagement, bridging the gap between business needs and technical solutions. Rather than replacing them, a more plausible scenario involves a symbiotic collaboration between business analysts and AI. AI tools can provide quick access to data and insights, allowing analysts to focus on higher-level thinking and strategic planning.
In conclusion, AI is unlikely to fully replace business analysts due to the complexity of their role. Instead, AI can be a valuable assistant, empowering analysts to work more efficiently and gain deeper insights. The future of business analysis lies in embracing AI as a complementary tool, enabling analysts to evolve and contribute strategically in an increasingly data-driven world. Collaboration between humans and AI will shape the role’s future, driving innovation and business success.
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